An efficient indoor large map global path planning for robot navigation

Large indoor cluttered environment representation is still a challenging task when non-uniform triangle cellbased or quadrangle cell-based decomposition is used to build the map. This paper aims at proposing a new method to represent a large indoor environment for efficient and global robot path planning using a trade-off among three criteria: path length, distance to obstacles, and path search complexity. In this regard, three steps are involved: (i) the design of a tiled map that represents several regular sub-regions of the given large environment; (ii) the selection of the best representation between non-uniform triangle cell-based submap and regular quadrangle cell-based submap for each tile using the predefined efficient path planning criteria. Hence, the entire large environment is represented by a hybrid cell-based map; (iii) the path search using the wellknown A* algorithm. Moreover, to find the best representation, firstly, a generative method based on cellular automata is used to build a large synthetic database of maps of the same size as the tiles. Each map of the database is associated with the representation which provides the most efficient path planning. Given a tile, the corresponding image is used to find the closed tile image from the database, and the associated representation is selected as the best representation. Extensive simulations, as well as experiments, suggest that for a given large, cluttered environment, the hybrid representation with a mix of triangle-based cells and quadrangles-based cells can provide a more efficient global path compared to the traditional regular quadrangle or triangle representations.